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Markov Chain Monte Carlo Simulation Model for Risk Assessment the Power Systems for Electromobility Use

机译:Markov Chain Monte Carlo风险评估的仿真模型电力计算机使用的电力系统

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摘要

A simulation model to evaluate risks in Power Systems including green energy sources to generate electricity for electro mobility use is presented in the paper. The model allows to calculate risk indicator that characterize the performance of the Power Systems. The model considers the additional risks of wind and solar variability in the Power Systems, through wind farms and PV farms, respectively. Also, in the recent years, the number of electric vehicles (EVs) on the road have been rapidly increasing. Charging this increasing number of EVs is expected to have an impact on the power grid especially if high charging powers and opportunistic charging are used. Multiple papers have observed that the charging stations are used by multiple users during the day. In a context where electric mobility is gaining increasing importance as a more sustainable solution for urban environments, this work presents the optimization of charging profiles of the potential users of these charging stations. We analyzed the charging profiles in a power grid with renewables sources of energy and we determine the optimal charging profiles for the power grid based on maximizing the energy delivered by renewable sources of energy.
机译:纸张中提出了一种评估电力系统中的电力系统风险的仿真模型,以发电用于电动迁移率使用。该模型允许计算表征功率系统性能的风险指示符。该模型分别考虑了电力系统,穿越风电场和光伏电池的电力系统中的风和太阳能变异性的额外风险。此外,在近年来,道路上的电动汽车(EVS)的数量迅速增加。收取该越来越多的EV,预计将对电网产生影响,特别是如果使用高充电功率和机会充电。已经观察到多篇论文认为,在白天使用多个用户使用充电站。在电动流动性在城市环境中获得更可持续的解决方案的情况下,该工作提出了这些充电站的潜在用户的充电轮廓的优化。我们分析了电网中的充电配置,具有可再生能源的能源,并且我们基于最大化可再生能源源传递的能量来确定电网的最佳充电配置。

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